spectral_embedding.ipynb

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   "source": [
    "# how fast is the spectral clustering method on sparse signed network\n",
    "from snpp.cores.spectral import build_laplacian_related_matrices_sparse\n",
    "from snpp.utils.matrix import load_sparse_csr\n",
    "from snpp.utils.signed_graph import g2m\n",
    "from snpp.utils.data import load_train_test_graphs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "loading train and test graphs...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 77357/77357 [00:01<00:00, 60157.08it/s]\n"
     ]
    }
   ],
   "source": [
    "dataset=\"slashdot\"\n",
    "train_g, test_g = load_train_test_graphs(dataset, False)\n",
    "train_m = g2m(train_g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "542274"
      ]
     },
     "execution_count": 14,
     "metadata": {},
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   ],
   "source": [
    "train_m.nnz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "W_p, W_n, D_p, D_n, D_hat = build_laplacian_related_matrices_sparse(train_m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy\n",
    "D_hat_inv = scipy.sparse.diags([1 / D_hat.diagonal()], offsets=[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "right_m = (D_p - W_p + W_n)\n",
    "\n",
    "L = D_hat_inv * right_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "504710"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L.nnz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "u, s, vt = scipy.sparse.linalg.svds(L, k=10, which='SM')"
   ]
  }
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